{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 拼接训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainadd(name):\n",
    "    data = pd.read_csv(name)\n",
    "    df = pd.DataFrame(data)\n",
    "    colname = list(df)\n",
    "    df.columns = colname\n",
    "    index_name = list(df.values[:,0])\n",
    "    #去掉第一行（\"公司\"）\n",
    "    df = df.drop(labels=\"公司\",axis = 1)\n",
    "    #将公司名改为Index\n",
    "    df.index = index_name\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>综合评分</th>\n",
       "      <th>总评名次</th>\n",
       "      <th>工人数</th>\n",
       "      <th>机器数</th>\n",
       "      <th>债券</th>\n",
       "      <th>工资系数</th>\n",
       "      <th>累计研发</th>\n",
       "      <th>累计分红</th>\n",
       "      <th>净资产</th>\n",
       "      <th>人均利润率</th>\n",
       "      <th>资本利润率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>飞翔2号</th>\n",
       "      <td>0.635</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>我的小白</th>\n",
       "      <td>0.465</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>突击队</th>\n",
       "      <td>0.437</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>just_play</th>\n",
       "      <td>0.377</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>霸气外露</th>\n",
       "      <td>0.265</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天涯之星</th>\n",
       "      <td>-0.795</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YYY.W</th>\n",
       "      <td>-0.795</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北经济学院创新管理A队</th>\n",
       "      <td>-0.795</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卡布奇诺</th>\n",
       "      <td>-1.156</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伍零壹玖</th>\n",
       "      <td>-2.155</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               综合评分   总评名次   工人数   机器数   债券   工资系数   累计研发   累计分红   净资产  \\\n",
       "飞翔2号          0.635      1     1     1    7      6      3      1     4   \n",
       "我的小白          0.465      2     1     1    4      2     10      3     1   \n",
       "突击队           0.437      3     1     1    6      3      3      3     2   \n",
       "just_play     0.377      4     1     1    2      4      9      3     3   \n",
       "霸气外露          0.265      5     1     1    3      1      3      2     7   \n",
       "...             ...    ...   ...   ...  ...    ...    ...    ...   ...   \n",
       "天涯之星         -0.795     10    12    12   12      8     12     10    10   \n",
       "YYY.W        -0.795     10    12    12   12      8     12     10    10   \n",
       "湖北经济学院创新管理A队 -0.795     10    12    12   12      8     12     10    10   \n",
       "卡布奇诺         -1.156     14    11    10   10      8      1     10    14   \n",
       "伍零壹玖         -2.155     15     1    11   11      8     11     10    15   \n",
       "\n",
       "               人均利润率   资本利润率  \n",
       "飞翔2号               4       2  \n",
       "我的小白               1       1  \n",
       "突击队                2       3  \n",
       "just_play          3       4  \n",
       "霸气外露               6       7  \n",
       "...              ...     ...  \n",
       "天涯之星              10      10  \n",
       "YYY.W             10      10  \n",
       "湖北经济学院创新管理A队      10      10  \n",
       "卡布奇诺              14      14  \n",
       "伍零壹玖              15      15  \n",
       "\n",
       "[270 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = []\n",
    "for i in range(2,11):\n",
    "    file_name = str(i)+'03.csv'\n",
    "    df = trainadd(file_name)\n",
    "    total.append(df)\n",
    "for i in range(2,11):\n",
    "    file_name = 'D:/Desktop/新建文件夹 (3)/13334/'+str(i)+\"03.csv\"\n",
    "    df = trainadd(file_name)\n",
    "    total.append(df)\n",
    "res = pd.concat(total)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = list(res.values[:,2:])\n",
    "y =list(res.values[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import tree\n",
    "crt = DecisionTreeRegressor(splitter='best',max_depth=3)\n",
    "crt = crt.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<graphviz.sources.Source at 0x27100346f10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "viewData = tree.export_graphviz(crt,\n",
    "feature_names = list(res)[2:],\n",
    "filled = True,\n",
    "rounded =True)\n",
    "graph = graphviz.Source(viewData)\n",
    "#graph.view()\n",
    "#graph.render('crt_tree') \n",
    "graph\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取新文件来测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(name):\n",
    "    data = pd.read_csv(name)\n",
    "    df = pd.DataFrame(data)\n",
    "    colname = list(df)\n",
    "    df.columns = colname\n",
    "    index_name = list(df.values[:,0])\n",
    "    #去掉第一行（\"公司\"）\n",
    "    df = df.drop(labels=\"公司\",axis = 1)\n",
    "    #将公司名改为Index\n",
    "    df.index = index_name\n",
    "    X_test = list(df.values[:,2:])\n",
    "    y_test =list(df.values[:,0])\n",
    "    return X_test,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8526449494057045\n",
      "[0.8972045216730922, 0.9518211283564523, 0.9111716268445056, 0.8621536428209434, 0.8619778748643263, 0.8280778067718153, 0.7934950332110529, 0.7152579607034479]\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "for i in range(2,10):\n",
    "    file_name = 'D:/Desktop/新建文件夹 (3)/13331/'+str(i)+\"03.csv\"\n",
    "    X_test,y_test = trans(file_name)\n",
    "    score = crt.score(X_test,y_test)\n",
    "    scores.append(score)\n",
    "print(np.mean(scores))\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对测试集评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7320318323202074, 0.7832063657757868, 0.8407390038570296, 0.8100544742250871, 0.8258096817844532, 0.785264666806426]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = []\n",
    "for i in range(6):\n",
    "    crt = DecisionTreeRegressor(splitter='best',max_depth=i+1)\n",
    "    crt = crt.fit(X,y)\n",
    "    scores = []\n",
    "    for i in range(2,10):\n",
    "        file_name = 'D:/Desktop/新建文件夹 (3)/13331/'+str(i)+\"03.csv\"\n",
    "        X_test,y_test = trans(file_name)\n",
    "        score = crt.score(X_test,y_test)\n",
    "        scores.append(score)\n",
    "    test.append(np.mean(scores))\n",
    "print(test)\n",
    "plt.plot(range(1,7),test,color='red',label=\"max_depth\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对训练集进行评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7688126379196967, 0.7254682039869581, 0.8012272005330707, 0.6481590160694951, 0.6706094873252278, 0.5785159581299637]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = []\n",
    "for i in range(6):\n",
    "    crt = DecisionTreeRegressor(max_depth=i+1)\n",
    "    crt = crt.fit(X,y)\n",
    "    test.append(crt.score(X_test,y_test))\n",
    "print(test)\n",
    "plt.plot(range(1,7),test,color='red',label=\"max_depth\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5203f191fb92a3c9962196e3c1c9bdaa035148bbce8d7674a8a57fbc91212ce7"
  },
  "kernelspec": {
   "display_name": "Python 3.8.9 64-bit",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
